Abstract: Embodiments of the present disclosure relate to systems and methods for controlling power generation plant operations via a human-machine interface. In one embodiment, a method can provide: receiving a user selection of a power generation plant component in a first navigation menu of a human-machine interface (HMI); based at least in part on the user selection, generating a second navigation menu of the HMI; receiving a subsequent user selection of a subcomponent in the second navigation menu of the HMI, wherein the subcomponent is associated with the power generation plant component; generating a control region for the HMI, the control region operable to manipulate the subcomponent; receiving a user input for the control region; and based at least in part on the user input, facilitating manipulation of one or more operations of the subcomponent.

Abstract: In one example embodiment, a gas turbine system includes a gas turbine, a compressor bleed valve, various sensors, and a valve health assessment system. The compressor bleed valve bleeds a portion of air received by the compressor during certain operating modes. The valve health assessment system includes a processor that uses a neural network model to generate a first failure mode predictor of the compressor bleed valve by processing operational data provided by the various sensors. The processor also generates a second failure mode predictor of the compressor bleed valve by using empirical data and a valve transfer function. The processor further generates a third failure mode predictor of the compressor bleed valve by using predictive data and a prediction model. The processor then applies a holistic procedure to the three failure mode predictors to derive a probability of occurrence of a failure in the compressor bleed valve.

Abstract: In one example embodiment, a gas turbine system includes a gas turbine, a compressor bleed valve, various sensors, and a valve health assessment system. The compressor bleed valve bleeds a portion of air received by the compressor during certain operating modes. The valve health assessment system includes a processor that uses a neural network model to generate a first failure mode predictor of the compressor bleed valve by processing operational data provided by the various sensors. The processor also generates a second failure mode predictor of the compressor bleed valve by using empirical data and a valve transfer function. The processor further generates a third failure mode predictor of the compressor bleed valve by using predictive data and a prediction model. The processor then applies a holistic procedure to the three failure mode predictors to derive a probability of occurrence of a failure in the compressor bleed valve.

Abstract: Embodiments of the present disclosure relate to systems and methods for controlling power generation plant operations via a human-machine interface. In one embodiment, a method can provide: receiving a user selection of a power generation plant component in a first navigation menu of a human-machine interface (HMI); based at least in part on the user selection, generating a second navigation menu of the HMI; receiving a subsequent user selection of a subcomponent in the second navigation menu of the HMI, wherein the subcomponent is associated with the power generation plant component; generating a control region for the HMI, the control region operable to manipulate the subcomponent; receiving a user input for the control region; and based at least in part on the user input, facilitating manipulation of one or more operations of the subcomponent.